Machine learning open-loop control of a mixing layer
We develop an open-loop control system using machine learning to destabilize and stabilize the mixing layer. The open-loop control law comprising harmonic functions is explored using the linear genetic programming in a purely data-driven and model-free manner. The best destabilization control law ex...
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| Published in | Physics of fluids (1994) Vol. 32; no. 11 |
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| Main Author | |
| Format | Journal Article |
| Language | English |
| Published |
Melville
American Institute of Physics
01.11.2020
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| Subjects | |
| Online Access | Get full text |
| ISSN | 1070-6631 1089-7666 |
| DOI | 10.1063/5.0030071 |
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| Abstract | We develop an open-loop control system using machine learning to destabilize and stabilize the mixing layer. The open-loop control law comprising harmonic functions is explored using the linear genetic programming in a purely data-driven and model-free manner. The best destabilization control law exhibits a square wave with two alternating duty cycles. The forced flow presents a 2.5 times increase in the fluctuation energy undergoing early multiple vortex-pairing. The best stabilization control law tames the mixing layer into pure Kelvin–Helmholtz vortices without following vortex-pairing. The 23% reduction of fluctuation energy is achieved under the dual high-frequency actuations. |
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| AbstractList | We develop an open-loop control system using machine learning to destabilize and stabilize the mixing layer. The open-loop control law comprising harmonic functions is explored using the linear genetic programming in a purely data-driven and model-free manner. The best destabilization control law exhibits a square wave with two alternating duty cycles. The forced flow presents a 2.5 times increase in the fluctuation energy undergoing early multiple vortex-pairing. The best stabilization control law tames the mixing layer into pure Kelvin–Helmholtz vortices without following vortex-pairing. The 23% reduction of fluctuation energy is achieved under the dual high-frequency actuations. |
| Author | Noack, Bernd R. |
| Author_xml | – sequence: 4 givenname: Bernd R. surname: Noack fullname: Noack, Bernd R. organization: 3Center for Turbulence Control, Harbin Institute of Technology, Shenzhen, Room 312, Building C, University Town, Xili, Shenzhen 518058, People’s Republic of China |
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| Cites_doi | 10.1063/1.5087540 10.1063/5.0006492 10.1017/jfm.2020.392 10.1007/s42241-020-0026-0 10.1017/s0022112001004827 10.1017/jfm.2020.785 10.1146/annurev.fl.27.010195.002111 10.1016/S0360-1285(96)00011-1 10.1063/1.3517297 10.1063/1.5115258 10.1017/jfm.2016.678 10.1007/s00348-019-2863-6 10.1007/s00348-018-2582-4 10.1146/annurev.fl.16.010184.002053 10.1017/s0022112008002073 10.1016/j.actaastro.2018.08.036 10.1063/5.0019299 10.1063/5.0022548 10.1007/s00348-003-0756-0 10.1146/annurev-fluid-010719-060214 10.1017/jfm.2014.355 10.1063/1.5116415 10.1063/1.5127202 10.1063/5.0020698 10.1063/1.5145276 |
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| SubjectTerms | Computational fluid dynamics Control theory Destabilization Fluid dynamics Fluid flow Genetic algorithms Harmonic functions Machine learning Physics Square waves |
| Title | Machine learning open-loop control of a mixing layer |
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